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Extracting the QRS Complexity and R Beats in Electrocardiogram Signals Using the Hilbert Transform

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ISCS 2013: Interdisciplinary Symposium on Complex Systems

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 8))

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Abstract

This paper presents a novel approach for the problem of detecting and extracting the QRS complex of electrocardiogram signals for different kinds of arrhythmias. First, an autocorrelation function is used in order to obtain the period of an electrocardiagram signal and then the Hilbert transform is applied to obtain R-peaks and beats. Twenty three different records extracted from the MIT-BIH arrhythmia database were used to validate the proposed approach. In this testing has been observed a 99.9 % of accuracy in detecting the QRS complexity, being a positive result in comparison with other recent researches.

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References

  1. Karsikas, M.: New methods for vectorcardiographic signal processing, Acta universitatis Oululensis, University of Oulu. PhD thesis, Oulu (2011)

    Google Scholar 

  2. Goya-Esteban, R., Barquero-Perez, O., Alonso-Atienza, F., Ervess, E., Requena-Carrion, J., Garcia-Albeola, A., Rojo-Alvarez, J.L.: A review on recent patents in digital processing for cardiac electric signals (I): from basic systems to arrhythmia analysis. Recent Pat. Biomed. Eng. 2, 22–31 (2009)

    Google Scholar 

  3. Asirvadam, V.S., Pisal, K.S., Izhar, L.I., Khuzi, N.A.A.M.: ECG viewed using grayscale patterns. In: Proceedings of the International Conference on Man-Machine Systems, pp. 11–13, Malaysia (2009)

    Google Scholar 

  4. Bagde, S., Raikwar, P.: Detection of QRS complexes of ECG waveform based on empirical mode decomposition using MATLAB. Inte. J. Eng. Innovative Technol. 1(1), 14–17 (2012)

    Google Scholar 

  5. Karsikas, M., Huikuri, H., Perkiömäki, J.S., Lehtola, L., Seppänen, T.: Influence of paper electrocardiogram digitizing on T wave and QRS complex morphology parameters. Ann. Noninvasive Electrocardiol. 12l, 282–290 (2007)

    Google Scholar 

  6. Lehtola, L., Karsikas, M., Koskinen, M., Huikuri, H., Seppänen, T.: Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG. J. Med. Eng. Technol. 32, 400–407 (2008)

    Article  Google Scholar 

  7. Zhao, Z., Yang, L., Chen, D., Luo, Y.: A human ECG identification system based on ensemble empirical mode decomposition. Sensors 13, 6832–6864 (2013)

    Article  Google Scholar 

  8. Shandilya, S., Ward, K., Kurz, M., Najarian, K.: Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning. BMC Med. Inform. Decis. Making 12(116), 1–9 (2012)

    Google Scholar 

  9. Niwas, I., Selva, S., Sadasivam, V.: Artificial neural network based automatic cardiac abnormalities classification. In: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications, pp. 41–46 (2009)

    Google Scholar 

  10. Illanes, A., Zhang, Q.: An algorithm for QRS onset and offset detection in single lead electrocardiogram records. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, pp. 541–544 (2007)

    Google Scholar 

  11. Benitez, D., Gaydecki, P., Zaidi, A., Fitzpatrick, A.: The use of the hilbert transform in ECG signal analysis. Comput. Biol. Med. 31, 399–406 (2001)

    Article  Google Scholar 

  12. Özbay, Y.: A new approach to detection of ECG arrhythmias: complex discrete wavelet transform based complex valued artificial neural network. J. Med. Syst. 33, 435–445 (2009)

    Article  Google Scholar 

  13. Ebrahimzadeh, A., Khazaee, A.: Detection of premature ventricular contractions using MLP neural networks: a comparative study. Measurement 43(1), 103–112 (2010)

    Article  Google Scholar 

  14. Monasterio, V., Laguna, P., Martnez, J.: Multilead analysis of T-Wave alternans in the ECG using principal component analysis. IEEE Trans. Biomed. Eng. 56, 1880–1890 (2009)

    Article  Google Scholar 

  15. Jezewski, J., Roj, D., Wrobel, J., Horoba, K.: A novel technique for fetal heart rate estimation from doppler ultrasound signal. Biomed. Eng. Online 10, 92–92 (2011)

    Article  Google Scholar 

  16. Zong, C., Chetouani, M.: Hilbert-Huang transform based physiological signals analysis for emotion recognition. In: Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, pp. 334–339 (2009)

    Google Scholar 

  17. Kohli, S., Makwana, N., Mishra, N., Sagar, B.: Hilbert transform based adaptive ECG R-Peak detection technique. Int. J. Electr. Comput. Eng. 2(5), 639–643 (2012)

    Google Scholar 

  18. Kentta, T., Karsikas, M., Juhani, M., Juha, S., Seppanen, T., Kiviniemi, A., Nieminen, T., Lehtimaki, T., Nikus, K., Lehtinen, R., Viik, J., Kahonen, M., Huikuri, H.: QRS-T morphology measured from exercise electrocardiogram as a predictor of cardiac mortality. Europace 13, 701–707 (2011)

    Google Scholar 

  19. Acar, B., Yi, G., Hnatkova, K.: Spatial, temporal and wavefront direction characteristics of 12-lead T-wave morphology. Med. biol. Eng. comput. 37, 574–584 (1999)

    Article  Google Scholar 

  20. MIT-BIH Database distribution, http://www.physionet.org/physiobank/database/mitdb

  21. Kotas, M.: Projective filtering of time-aligned ECG beats for repolarization duration measurement. Comput. Methods Program Biomed. 85(2), 115–123 (2007)

    Article  Google Scholar 

  22. Yeh, Y.C., Wang, W.J.: QRS complex detection for ECG signal: the difference operation method. Comput. Methods Program Biomed. 91(3), 245–254 (2008)

    Article  MathSciNet  Google Scholar 

  23. Prakash, J.: Analysis of ECG signal for Detection of Cardiac Arrhythmias. National Institute Of Technology, Master of technology thesis, Rourkela (2011)

    Google Scholar 

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Correspondence to Ricardo Rodríguez .

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Rodríguez, R., Mexicano, A., Cervantes, S., Bila, J., Ponce, R. (2014). Extracting the QRS Complexity and R Beats in Electrocardiogram Signals Using the Hilbert Transform. In: Sanayei, A., Zelinka, I., Rössler, O. (eds) ISCS 2013: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45438-7_20

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  • DOI: https://doi.org/10.1007/978-3-642-45438-7_20

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  • Online ISBN: 978-3-642-45438-7

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